Integrated sensor network methods and systems

ABSTRACT

Methods and systems for an integrated sensor network are described. In one embodiment, sensor data may be accessed from a plurality of motion sensors and a bed sensor deployed in a living unit for a first time period. An activity pattern for the first time period may be identified based on at least a portion of sensor data associated with the first time period. The activity pattern may represent a physical and cognitive health condition of a person residing in the living unit. Additional sensor data may be accessed from the motion sensors and the bed sensor deployed for a second time period. A determination of whether a deviation of the activity pattern of the first time period has occurred for the second time period may be performed. An alert may be generated based on a determination that the derivation has occurred. In some embodiments, user feedback is captured on the significance of the alerts, and the alert method is customized based on this feedback. Additional methods and systems are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of United States Provisional patentapplication entitled “Monitoring System for Eldercare”, Ser. No.61/217,623, filed 1 Jun. 2009, the entire contents of which is hereinincorporated by reference.

GRANT STATEMENT

This invention was made with government support under Grant No.IIS-0428420 and, Grant No. 90AM3013 awarded by the U.S. Administrationon Aging, and Grant No. 1R21NR011197-01 awarded by the NationalInstitute of Health. The government has certain rights in the invention.

FIELD

This application relates to methods and systems for sensor networks, andmore specifically to methods and systems for integrated sensor networks.

BACKGROUND

Countries on multiple continents are experiencing an aging population.The number of older adults is growing dramatically. With thisdemographic shift, there is a desire to keep older adults healthy,functionally able, and living independently, in part because thisprovides a better quality of life, and in part because the agingpopulation will stress current facilities and resources designed to carefor elders. Challenges exist in keeping people healthy and functionallyable as they age.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 are block diagrams of example systems, according toexample embodiments;

FIG. 3 is a block diagram of an example operator device that may bedeployed within the system of FIG. 1, according to an exampleembodiment;

FIG. 4 is a block diagram of an example provider device that may bedeployed within the system of FIG. 1, according to an exampleembodiment;

FIG. 5 is a block diagram of an example sensor processing subsystem thatmay be deployed within the operator device of FIG. 3 or the providerdevice of FIG. 4, according to an example embodiment;

FIGS. 6-8 are block diagrams of flowcharts illustrating methods forsensor processing, according to example embodiments;

FIGS. 9 and 10 are block diagrams of flowcharts illustrating methods fordisplay generation, according to example embodiments;

FIG. 11 is a block diagram of a flowchart illustrating a method fordetermining dis-similarity of density maps, according to an exampleembodiment;

FIG. 12 is a block diagram of a flowchart illustrating a method forperforming cluster analysis, according to an example embodiment;

FIGS. 13-27 are diagrams, according to example embodiments; and

FIG. 28 is a block diagram of a machine in the example form of acomputer system within which a set of instructions for causing themachine to perform any one or more of the methodologies discussed hereinmay be executed.

DETAILED DESCRIPTION

Example methods and systems for an integrated sensor network aredescribed. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of example embodiments. It will be evident, however, toone of ordinary skill in the art that embodiments of the invention maybe practiced without these specific details.

Most adults would prefer to remain as active as possible and to liveindependently in unrestricted environments as they age. However, becausechronic illness and declining health affect most people as they getolder, placement in more restricted housing environments like assistedliving or nursing homes is fairly common. The reason this sort ofplacement occurs is because health assessments and medical care havetraditionally required face to face meetings.

One alternative consideration for monitoring older adults includes theuse of smart sensor technologies as part of an integrated sensor networkthat detects activity levels around them and electronically sends theactivity data to a central repository. Through web technologies, datacan be accessed and viewed by health care providers, families or othersinterested in the health of the older person being monitored.

The integrated sensor network includes simple motion sensors, a stovesensor, video sensors, and a bed sensor that captures sleep restlessnessand pulse and respiration levels. Patterns in the sensor data mayrepresent physical and cognitive health conditions. Recognition may beperformed when activity patterns begin to deviate from the norm.Performing the recognition may enable early detection of potentialproblems that may lead to serious health events if left unattended.

FIG. 1 illustrates an example system 100 in which an integrated sensornetwork may be used. The system 100 is an example platform in which oneor more embodiments of the methods may be used. However, the integratedsensor network may also be used on other platforms.

An operator may use the integrated sensor network by using the operatordevice 102. The integrated sensor network may be used by a personresiding in a living unit. The operator device 102 may be located in theliving unit, outside of the living unit but in a living unit community,or a location outside of the living unit community. Examples ofoperators include clinicians, researchers, and the like.

The operator may use the operator device 102 as a stand-alone device touse the integrated sensor network, or may use the operator device 102 incombination with a provider device 106 available over a network 104. Insome embodiments, the provider device 106 is also under the control ofthe operator but at a location outside of the living unit community.

The operator device 102 may be in a client-server relationship with theprovider device 106, a peer-to-peer relationship with the providerdevice 106, or in a different type of relationship with the providerdevice 106. In one embodiment, the client-server relationship mayinclude a thin client on the operator device 102. In another embodiment,the client-server relationship may include a thick client on theoperator device 102.

The network 104 over which the operator device 102 and the providerdevice 106 may communicate include, by way of example, a MobileCommunications (GSM) network, a code division multiple access (CDMA)network, 3rd Generation Partnership Project (3GPP), an Internet Protocol(IP) network, a Wireless Application Protocol (WAP) network, a WiFinetwork, or an IEEE 802.11 standards network, as well as variouscombinations thereof. Other conventional and/or later developed wiredand wireless networks may also be used.

In one embodiment, the provider device 106 is a single device. In oneembodiment, the provider device 106 may include multiple computersystems. For example, the provider device 106 may include multiplecomputer systems in a cloud computing configuration.

Multiple sensors 108 forming a sensor network are included in the system100 to obtain sensor data 112. Examples of sensors 108 include motionsensors, a bed sensor, and a stove sensor. In general, the multiplesensors 108 are passive, nonwearable sensors.

The operator device 102, the provider device 106, or both maycommunicate with a database 110. The database 110 may contain sensordata 112, health data 114, and generated data 116.

The sensor data 112 may be received from the sensors 108 or otherwiseaccessed (e.g., indirectly accessed by the provider 106 from theoperator device 102). The health data 114 includes health relatedinformation about people. In general, the health data 114 is for thepeople associated with a particular doctor, healthcare organization,and/or living unit community. The generated data 116 includesinformation received and stored based on use of the integrated network.

FIG. 2 illustrates an example system 200, according to an exampleembodiment. The system 200 is a specific example of the system 100. Asshown in the system 200, the sensor data 112 is received by the operatordevice 102 from the sensors 108 and stored in a database 202. Theoperator device 102 is a logging device that simply collects the sensordata 112 and does not regularly receive input from the person, theoperator, or otherwise.

The operator device 102 transmits the sensor data 112 to the providerdevice 106 for storage in the database 110 on a regular basis. Thesensor data 112 may be transmitted, hourly, daily, weekly, or at othergreater or lesser time increments. The provider device 106 of the system200 may include multiple provider devices including client providerdevices and server provider devices. The operator may communicate with aserver provider device through a user interface or otherwise.

FIG. 3 illustrates an example operator device 102 that may be deployedin the system 100 (see FIG. 1), or otherwise deployed in another system.The operator device 102 is shown to include a signal processingsubsystem 302 to enable use of the integrated sensor network.

FIG. 4 illustrates an example provider device 106 that may be deployedin the system 100 (see FIG. 1), or otherwise deployed in another system.The provider device 106 is shown to include a signal processingsubsystem 302 to enable use of the integrated sensor network.

In one embodiment, the functionality that enables use of the integratedsensor network voxel model resides solely on the sensor processingsubsystem 302 deployed in the operator device 102. In anotherembodiment, the functionality resides solely on the sensor processingsubsystem 302 deployed in the provider device 106. In anotherembodiment, the functionality is partially performed on the sensorprocessing subsystem 302 deployed in the operator device 102 andpartially performed on the sensor processing subsystem 302 deployed inthe provider device 106. The functionality may otherwise be distributedamong the operator device 102, the provider device 106, or anotherdevice.

FIG. 5 illustrates an example sensor processing subsystem 302 that maybe deployed in the operator device 102, the provider device 106, orotherwise deployed in another system. One or more modules are includedin the sensor processing subsystem 302 to process the sensor data 112.The modules of the signal processing subsystem 302 that may be includedare a sensor data module 502, an activity pattern identification module504, a deviation module 506, an alert module 508, a health data module510, a parameter calculation module 512, a feedback module 514, acorrelation module 516, a change determination module 518, a displaygeneration module 520, a density module 522, and/or a clustering module524. Other modules may also be included. In various embodiments, themodules may be distributed so that some of the modules may be deployedin the operator device 102 and some of the modules may be deployed inthe provider device 106. In one particular embodiment, the signalprocessing subsystem 302 includes a processor, memory coupled to theprocessor, and a number of the aforementioned modules deployed in thememory and executed by the processor.

The sensor data module 502 accesses the sensor data 112. The sensor data112 may be associated with motion sensors deployed in a living unit, abed sensor deployed in a living unit, a stove sensor deployed in theliving unit, other environmentally-mounted, nonwearable sensors, orcombinations thereof. In general, the sensors 108 are passive,non-wearable sensors. The sensor data 112 accessed by the sensor datamodule 502 may be for a time period.

In some embodiments, the living unit is an apartment. In otherembodiments, the living unit is a house.

The activity pattern identification module 504 identifies an activitypattern for the time period based on at least a portion of the sensordata 112 accessed by the sensor data module 502. In some embodiments,the activity pattern represents a physical and cognitive healthcondition of a person living in the living unit. In one embodiment,activity pattern includes a single feature. In another embodiment, theactivity pattern includes multiple features.

In some embodiments, the sensor data module 502 identifies at least aportion of the sensor data 112 associated with the time period as beingassociated with the person. Identification of the activity pattern forthe first time period is based on at least the portion of the sensordata 112 associated with the first time period associated with theperson.

In some embodiments, the sensor data module 502 accesses additionalsensor data 112 for an additional time period that occurs after a firsttime period. The deviation module 506 may then determine whether adeviation of the activity pattern of the first time period has occurredfor the additional time period. The alert module 508 generates an alertbased on a determination that the derivation has occurred.

In some embodiments, the alert module 508 transmits the alert. In someembodiments, the alert module 508 stores the alert. The alert module 508may otherwise use or process the alert.

In some embodiments, event listeners (the observers) register with anevent provider associated with the alert module 508 to be notified ofsensor events (the changes). The event provider may support a filteringoperation. That is, a template for the sensor events can be specified sothat event listeners are only notified if a sensor event matches thetemplate.

The alert module 508 provides a cohesive yet flexible mechanism forincorporating different types of alert conditions. State machines may beused by alert providers to model alert specifications. As sensor eventsare observed, an alert model associated with the alert module 508 maytransition to a new state and, if warranted, will generate an alertcondition.

Timers may be included for state transitions. The state machinegeneralization supports simple one-sensor alerts as well as alerts thatinvolve more complex interactions among multiple sensors. The alertmodule 508 easily accepts inputs from multiple sources. Sensor eventsmay be replayed from the database 110 through the use of the generateddata 116, to facilitate testing of alert algorithms. Alerts may be sentto different output streams, including a pager system for immediatealerts as well as emailed alerts for daily summaries.

In some embodiments, the activity pattern identification module 504identifies the activity pattern for the second time period based onaccess of the additional sensor data 112 associated with the second timeperiod. The determination of whether the deviation has occurred by thedeviation module 506 may then include determining whether the deviationof the activity pattern of the second time period from the activitypattern of the first time period exceeds a threshold.

In some embodiments, the health data module 510 analyzes health dataassociated with the person. Generation of the alert by the alert module508 may be based on when the activity pattern of the second time periodfrom the activity pattern of the first time period exceeds the thresholdand analysis of the health data 114.

In some embodiments, the parameter calculation module 512 calculatesstatistical parameters of at least a portion of the sensor data 112 forthe time period. A determination of whether the deviation has occurredby the deviation module 506 may then include determining whether atleast a portion of the additional sensor data 112 for the additionaltime period is outside of a threshold based on the statisticalparameters.

The alert generated by the alert module 508 may be a hits-based alert.In one embodiment, the activity pattern for the time period is based ontotal number of sensor hits of a sensor 108 during a day of the timeperiod.

The alert generated by the alert module 508 may be a time-based alert.In one embodiment, the activity pattern for the time period is based ontotal time that the sensor 108 fired during a particular day of the timeperiod.

In some embodiments, the alert module 508 transmits the alert includinga link to a web interface. In one embodiment, the web interface includesthe sensor data 112 of the second time period in the context of thesensor data 112 of the first time period.

The feedback module 514 may be deployed in the sensor processingsubsystem 302 to receive and process feedback, requests, selections, orthe like.

In some embodiments, the feedback module 514 receives a feedbackresponse to the alert. The feedback response includes feedback regardingclinical relevance of the alert. The feedback module 514 may then takeaction based on receipt of the feedback response.

In one embodiment, the action includes adjusting the threshold based onthe receipt of the feedback response. In one embodiment, the actionincludes recording ignored indicia for the person based on the receiptof the feedback response. The ignored indicia may be associated with afeature of the alert.

In some embodiments, the sensor processing subsystem 302 includes thecorrelation module 516 and the change determination module 518 topredict changes in a health condition. The health condition may be aphysical condition, a mental condition, or a physical and a mentalcondition. In one embodiment, the health condition is pulse pressure.Pulse pressure may be the difference between systolic blood pressure(SBP) and the diastolic blood pressure (DBP).

By way of example, the sensor data module 502 accesses the sensor data112 associated with a person and the health module data module 510accessing the health data 114 of the person for a first time period. Thecorrelation module 516 then correlates the health data to at least aportion of the sensor data 112 for the first time period. The sensordata module 502 accesses additional sensor data 112 for a second timeperiod.

The changer determination module 518 then determines whether a change ina health condition of the person has occurred based on the additionalsensor data 112 and correlation of the health condition data to at leastthe portion of the sensor data 112 for the first time period. The alertmodule 508 may generate an alert when a determination is made that thechange in the health condition has occurred.

The display generation module 520 generates a display. In someembodiments, the alert module 508 generates the alert and the displaygeneration module 520 generates a display based the alert.

In some embodiments, the sensor data module 502 accesses the sensor data112 and the display generation module 520 generates a display based onthe sensor data 112. In one embodiment, the sensor data 112 is groupedon the display based on multiple categories. The categories may include,by way of example, motion, pulse, breathing, and bed restlessness.

In some embodiments, the feedback module 514 receives a selection of aperson and a date range. The sensor data module 502 may then access thesensor data 112 based on receipt of the selection.

A user may interface with the sensor processing subsystem 302 to zoom inor zoom out on the display. In some embodiments, the feedback module 514receives a time interval modification request. The display generationmodule 520 may then generate a display based on access of the sensordata 112 associated with the time period and receipt of the timeinterval modification request.

In some embodiments, the feedback module 514 receives a time incrementmodification request. The display generation module 520 may thengenerate the display based on access of the sensor data 112 associatedwith the time period and receipt of the time increment modificationrequest.

The density module 522 determines an away-from-home time period for aperson associated with the living unit during the time period. Thegeneration of the display by the display generation module 520 thengenerates the display based on access of the sensor data 112 and adetermination of the away-from-home time period.

In some embodiments, a determination of the away-from home time periodby the density module 522 includes analyzing the sensor data 112 todetermine whether a living unit departure sensor sequence and a livingunit return sensor sequence has occurred and calculating a timedifference between occurrence of the living unit departure sensorsequence and occurrence of the living unit return sensor sequence.

In one embodiment, analyzing the sensor data 112 includes applying fuzzylogic to at least a portion of the sensor data 112 to determine whethera living unit departure sensor sequence and a living unit return sensorsequence has occurred.

In some embodiments, the density module 522 computes a number of motionsensor hits for multiple hours. A sensor hit is associated with a motionsensor. The density module 522 may then calculate density for themultiple hours. The generation of the display by the display generationmodule 520 may then be based on calculation of the density.

In one embodiment, the display generation module 520 selects colormappings and then generates the display based on the selection of colormappings. In general, a color mapping has a color based on the densityand is associated with a position on a display based on the hour andday.

Dis-similarity between density maps may be computed by use of thedensity module 522. In some embodiments, the density module 522 accessesa first density map and a second density map, the first density maphaving a first color mappings, the second density map having a secondcolor mappings, computes a dis-similarity between the first density mapand the second density map based on a textual feature of the firstdensity map and the second density map, and generates a computationalresult based on computing the dis-similarity. Textual features mayinclude, by way of example, spatial, frequency, and perceptualproperties. The display generation module 520 may then generate adisplay based on computation of the dis-similarity. The density module520 may transmit a notification based on computation of thedis-similarity, storing the computational result, or both.

Clustering may be performed by the clustering module 524 to analyze thesensor data 112 based on clusters. In some embodiments, the clusteringmodule 524 generates feature clusters for a time period. A featurecluster is associated with multiple feature vectors, wherein a featurevector is associated with the sensor data 112 from at least some ofmotion sensors and/or a bed sensor. The sensor data module 502 accessesadditional sensor data 112 associated a feature for a different timeperiod. The clustering module 524 may then determine whether theadditional sensor data 112 falls within the feature clusters or belongsin a new cluster.

Based on a result of the determination, the clustering module 524generates a notification. The notification may be a cluster additionnotification based on a determination that the additional sensor data112 falls within the feature clusters. The notification may be a newcluster notification based on a determination that the additional sensordata 112 belongs in the new cluster.

FIG. 6 illustrates a method 600 for sensor processing according to anexample embodiment. The method 600 may be performed by the operatordevice 102 or the processor device 106 of the system 100 (see FIG. 1),or may be otherwise performed.

At block 602, the sensor data 112 is accessed from the motion sensorsand the bed sensor deployed in a living unit for a first time period. Ingeneral, the deployed sensors are passive, non-wearable sensors.

At least a portion of the sensor data 112 associated with the first timeperiod may be identified as being associated with the person at block604.

An activity pattern is identified for the first time period at block 606based on at least a portion of sensor data 112 associated with the firsttime period. In one embodiment, the activity pattern represents aphysical and cognitive health condition of a person residing in theliving unit. In some embodiments, identification of the activity patternfor the first time period is based on at least the portion of sensordata 112 associated with the first time period associated with theperson.

At block 608, additional sensor data 112 is accessed from the motionsensors and the bed sensor deployed in the living unit for a second timeperiod. The second time period occurs after the first time period. Insome embodiments, the first period of time has a same time duration asthe second period of time.

In one embodiment, the first time period is for a period of fourteenconsecutive days and the second time period is for a period of a singleday. Different periods of time may be used for the first time period andthe second time period.

In some embodiments, the operations performed at block 602 includingaccessing the sensor data 112 from a stove sensor deployed in the livingunit and the operations performed at block 608 include accessing theadditional sensor data 112 from the stove sensor deployed in the livingunit.

A determination of whether a deviation of the activity pattern of thefirst time period has occurred for the second time period is performedat block 610. In some embodiments, the activity pattern includesmultiple features and the deviation is associated with a feature of themultiple features.

In some embodiments, the activity pattern for the second time period isidentified based on access of the additional sensor data 112 associatedwith the second time period. The determination performed at block 610may then include determining whether the deviation of the activitypattern of the second time period from the activity pattern of the firsttime period exceeds a threshold.

The health data 114 associated with the person may be analyzed at block612, while an alert is generated at block 614. In some embodiments, thealert is generated based on a determination that the derivation hasoccurred. In some embodiment, the alert is generated based on when theactivity pattern of the second time period from the activity pattern ofthe first time period exceeds the threshold and analysis of the healthdata 114. In some embodiments, the alert is transmitted, while in someembodiments the alert is stored.

FIG. 7 illustrates a method 700 for sensor processing according to anexample embodiment. The method 70 may be performed by the operatordevice 102 or the processor device 106 of the system 100 (see FIG. 1),or may be otherwise performed.

At block 702, the sensor data 112 is accessed from motion sensors and abed sensor deployed in a living unit for a first time period.

An activity pattern for the first time period is identified at block 704based on at least a portion of the sensor data 112 associated with thefirst time period. In some embodiments, the activity pattern representsa physical and cognitive health condition of a person residing in theliving unit. In one embodiment, the activity pattern includes a singlefeature. In another embodiment, the activity pattern includes multiplefeatures.

At block 706, additional sensor data 112 is accessed from the motionsensors and the bed sensor deployed in the living unit for a second timeperiod. The second time period occurs after the first time period.

Statistical parameters of at least a portion of the sensor data 112 forthe first time period are calculated at block 708.

A determination of whether a deviation of the activity pattern of thefirst time period has occurred for the second time period is performedat block 710. In some embodiments, the determination includesdetermining whether at least a portion of the additional sensor data 112for the second time period is outside of a threshold. In general, thethreshold is based on the statistical parameters.

An alert is generated at block 712 based on a determination that thederivation has occurred. In some embodiments, the alert is a hits-basedalert. The activity pattern for the first time period may then be basedon total number of sensor hits of a particular sensor 108 during aparticular day of the first time period. In some embodiments, the alertis a time-based alert. The activity pattern for the first time periodmay then based on total time that a particular sensor 108 fired during aparticular day of the first time period.

In some embodiments, the alert generated may be adapted or customizedbased on received feedback.

The alert including a link to a web interface may be transmitted atblock 714. The web interface may include the sensor data 112 of thesecond time period in the context of the sensor data 112 of the firsttime period.

A feedback response may be received to the alert at block 716. Thefeedback response includes feedback regarding clinical relevance of thealert.

An action may be taken at block 718 based on receipt of the feedbackresponse. In some embodiments, taking the action may include adjustingthe threshold based on the receipt of the feedback response. In someembodiments, taking the action may include recording ignored indicia forthe person based on the receipt of the feedback response. The ignoredindicia may be associated with a feature of the alert.

FIG. 8 illustrates a method 800 for sensor processing according to anexample embodiment. The method 800 may be performed by the operatordevice 102 or the processor device 106 of the system 100 (see FIG. 1),or may be otherwise performed.

The health data 114 of a person for a first time period is accessed atblock 802.

The sensor data 112 from motion sensors and a bed sensor deployed in aliving unit for the first time period is accessed at block 804.

The health data is correlated to at least a portion of the sensor data112 for the first time period at block 806.

Additional sensor data 112 is accessed at block 808 from the motionsensors and the bed sensor deployed in the living unit for a second timeperiod. The second time period generally occurs after the first timeperiod.

At block 810, a determination of whether a change in a health conditionof the person has occurred is made based on the additional sensor data112 and correlation of the health data 114 to at least the portion ofthe sensor data 112 for the first time period.

An alert may be generated at block 812 when a determination is made thatthe change in the health condition has occurred.

FIG. 9 illustrates a method 900 for display generation according to anexample embodiment. The method 900 may be performed by the operatordevice 102 or the processor device 106 of the system 100 (see FIG. 1),or may be otherwise performed.

A selection of a person and/or a date range may be received at block902.

The sensor data 112 is accessed from motion sensors and a bed sensordeployed in a living unit for a time period at block 904. In someembodiments, the access of the sensor data 112 from the motion sensorsand the bed sensor for the time period is based on receipt of theselection.

A request may be received at block 906. In some embodiments, the requestis a time interval modification request. In some embodiments, therequest is a time increment modification request.

A display is generated at block 908 based on access of the sensor data112 associated with the time period. In some embodiments, the sensordata 112 is grouped on the display based on multiple categories. Forexample, the categories may include motion, pulse, breathing, andrestlessness.

In some embodiments, generation of the display is based on access of thesensor data 112 associated with the time period and receipt of the timeinterval modification request. In some embodiments, generation of thedisplay is based on access of the sensor data 112 associated with thetime period and receipt of the time increment modification request.

FIG. 10 illustrates a method 1000 for display generation according to anexample embodiment. The method 1000 may be performed by the operatordevice 102 or the processor device 106 of the system 100 (see FIG. 1),or may be otherwise performed.

A selection of a person and/or a date range may be received at block1002.

The sensor data 112 is accessed from motion sensors and a bed sensordeployed in a living unit for a time period at block 1004. In someembodiments, the access of the sensor data 112 from the motion sensorsand the bed sensor for the time period is based on receipt of theselection.

A determination of an away-from-home time period for a person associatedwith the living unit during the time period is made at block 1006. Insome embodiments, the determination of the away-from-home time periodincludes analyzing the sensor data 112 to determine whether a livingunit departure sensor sequence and a living unit return sensor sequencehas occurred and calculating a time difference between occurrence of theliving unit departure sensor sequence and occurrence of the living unitreturn sensor sequence. The away-from home time period may then based onthe time difference.

In one embodiment, analyzing the sensor data 112 includes applying fuzzylogic to at least a portion of the sensor data 112 to determine whethera living unit departure sensor sequence and a living unit return sensorsequence has occurred.

A number of motion sensor hits for multiple hours of the time period maybe computed at block 1008. A single motion sensor hit is associated witha single motion sensor of the plurality of motion sensors.

A density for the hours may be calculated at block 1010. The density foran hour may be based on the number of motion sensor hits during the hourand the determination of the away-from-home time period.

A display is generated at block 1012 based on access of the sensor data112 associated with the time period and a determination of theaway-from-home time period. In some embodiments, generation of thedisplay is based on calculation of the density.

In some embodiments, generation of the display includes selecting acolor mappings and generating the display based on selection of thecolor mappings. In general, a color mapping has a color based on thedensity and is associated with a position based on an hour of a day.

FIG. 11 illustrates a method 1100 for determining dis-similarity ofdensity maps according to an example embodiment. The method 1100 may beperformed by the operator device 102 or the processor device 106 of thesystem 100 (see FIG. 1), or may be otherwise performed.

A dis-similarity measure based on texture features may be used forcomparing density maps and automatically determining changes in activitypatterns. The dis-similarity between two density maps may be computed toaid caregivers in evaluating changes of residents. The texture featuresmay be used evaluate the dis-similarity of density maps by capturingspatial, frequency, and perceptual properties such as periodicity,coarseness, and complexity. Texture features may be extracted using theco-occurrence distribution (e.g., the gray-level co-occurrencestatistical method using the density values directly).

In some embodiments, the density maps need not have the color mapping todetermine the dis-similarity.

A first density map and a second density map are accessed at block 1102.The first density map has first color mappings. The second density maphas second color mappings. In general, a color mapping has a color basedon density and is associated with a position based on an hour of a day.In some embodiments, the density is based on a number of motion sensorhits during the hour and a determination of the away-from-home timeperiod.

A dis-similarity between the first density map and the second densitymap is computed at block 1104 based on a textual feature of the firstdensity map and the second density map. Examples of textual featuresinclude spatial, frequency, and perceptual properties. The computationmay be performed based on a single textual feature or multiple textualfeatures.

An angular second moment feature (ASM) may measure homogeneity of theimage. The contrast feature may measure the amount of local variationsin an image. The inverse difference moment may also measure imagehomogeneity. Entropy may measure the disorder. Other non-textualfeatures may also be used to discriminate the dis-similarity of densitymaps. For example, average motion density per hour and average time awayfrom the living unit per day may be used during the computationperformed at block 1104.

The dis-similarity of two different density maps, in some embodiments,is represented by a number that is computed in feature space as thedistance from one map to another.

A computational result is generated at block 1106 based on computing thedis-similarity.

A display may be generated at block 1108 based on computation of thedis-similarity. In some embodiments, a notification based on computationof the dis-similarity may be transmitted. In some embodiments, thecomputational result may be stored.

FIG. 12 illustrates a method 1200 for performing cluster analysisaccording to an example embodiment. The method 1200 may be performed bythe operator device 102 or the processor device 106 of the system 100(see FIG. 1), or may be otherwise performed.

Feature clusters are generated for a time period at block 1202. The timeperiod includes multiple days. A feature cluster is associated with amultiple feature vectors. A feature vector is associated with the sensordata 112 from at least some of the motion sensors and/or a bed sensordeployed in a living unit.

Additional sensor data 112 associated with a particular feature for adifferent time period is accessed at block 1204.

A determination of whether the additional sensor data 112 falls withinthe feature clusters or belongs in a new cluster is made at block 1206.

A notification is generated at block 1208 based on a result of adetermination.

In some embodiments, a cluster addition notification is generated basedon a determination that the additional sensor data 112 falls within thefeature clusters. In some embodiments, a new cluster notification isgenerated based on a determination that the additional sensor data 112belongs in the new cluster.

FIG. 13 is a diagram 1300 of a user interface, according to an exampleembodiment. The user interface shows motion sensor data for multiplesensors over a period of fourteen days.

FIG. 14 is a diagram 1400 of a user interface, according to an exampleembodiment. The user interface shows motion sensor data over a period oftwenty eight days. The diagram 1400 is a “zoomed out” version of thediagram 1300 (see FIG. 13).

FIG. 15 is a diagram 1500 of a user interface, according to an exampleembodiment. The user interface shows motion sensor data over a period ofa day. The diagram 1500 is a “zoomed in” version of the diagram 1300(see FIG. 13).

FIG. 16 is a diagram 1600 of a user interface, according to an exampleembodiment. The user interface shows motion sensor data for a singlesensor over a period of fourteen days.

FIG. 17 is a diagram 1700 of an example alert, according to an exampleembodiment. The alert shown the diagram 1700 may be transmitted as ane-mail or otherwise transmitted. The alert is shown to include links toa user interface associated with sensors. The links included in thediagram are a link to a bathroom sensor, a kitchen sensor, and a livingroom sensor. In the example alert shown in the diagram 1700, links arealso included to feedback web pages to capture a user's rating of thesignificance of the alert.

FIG. 18 is a diagram 1800 of a user interface, according to an exampleembodiment. The diagram 1800 shows a user interface that may bepresented based on selection of a link included in an alert of thediagram 1700.

As shown in the diagram 1800, a resident ID, a time period includingstarting date, starting hour, ending date, and ending hour, a timeinterval, and an increment selections may be available forcustomization. The operator may modify default selections and then pressa submit button.

FIG. 19 is a diagram 1900 of a user interface, according to an exampleembodiment. The diagram 1900 shows sensor firing data for a fourteen dayperiod. The diagram 1900 may be presented based on selection of a submitbutton from the diagram 1800.

FIG. 20 is a diagram 2000 of a user interface, according to an exampleembodiment. The diagram enables an operator to provide alert feedback.The operator may include a rating of the significance of the alert,thoughts about the alert (e.g., not enough of a change and not a goodparameter), and comments through the user interface. Other or differentfeedback may be collected. The operator may also designate theperspective (e.g., classification) of the operator submitting thefeedback.

In some embodiments, the user interface shown in the diagram 2000 may beused to provide adaptive, customizable alerts by adjusting the sensorparameters and thresholds, based on the alert feedback ratings.

FIGS. 21-23 are diagrams 2100-2300 of density maps, according to anexample embodiment. While the diagrams 2100-2300 are shown in thisdocument in black and white, the displays associated with the diagrams2100-2300 are typically generated in color based on color mappings.

The diagram 2100 is a density map of a person with a sedentary lifestylepattern for one month. The diagram 2200 is a density map of a personwith an active lifestyle pattern for one month. The diagram 2300 is adensity map of a person with an irregular lifestyle pattern showing acognitive problem for one month.

By monitoring the motion density maps over time, health care providersin some embodiments may identify a typical pattern of activity for anindividual and watch for changes in the pattern

FIG. 24 is a diagram 2400 of a floor plan of living unit, according toan example embodiment. The diagram 2400 shows example locations ofmotion sensors, a bed sensor, and a stove sensor in the living unit.

The motion sensors may detect presence in a particular room as well asspecific activities. For example, a motion sensor installed on theceiling above the shower detects showering activity; motion sensorsinstalled discretely in cabinets and the refrigerator detect kitchenactivity. For convenience, a motion sensor may also installed on theceiling above the door of the living unit, to detect movement in and outof the doorway (e.g., for living unit exits). The motion sensors, insome embodiments, are commercially available passive infrared (PIR)sensors which transmit using the wireless X10 protocol. Other types ofsensors may be used.

In some embodiments, the sensors detect movement of warm bodies andtransmit an event about every 7 seconds when movement is still detected.This artifact is useful for capturing a general lifestyle pattern; forexample, a sedentary pattern will result in a smaller number of sensorevents over time compared to a more active “puttering” pattern.

The bed sensor may be a transducer which detects presence in the bed,pulse and respiration rates, and bed restlessness. Pulse and respirationrates may be reported as low, normal, and high, based on thresholds, orpulse and respiration rates may be reported as numerical rates. In someembodiments, bed restlessness is reported based on the persistence ofmovement in the bed. All of the output of the bed sensor may contributeto the general pattern of the resident.

The stove sensor may detect motion in the kitchen as well as thetemperature of the stove/oven unit. This may be performed through amodified X10 PIR motion sensor. When a high temperature is detected, a“stove on” event may be generated. When the temperature drops below athreshold again, a “stove off” event may be generated. This sensor isincluded so that an alert could be generated if the stove is left on andthere is no indication of someone in the kitchen for a specified periodof time.

In some embodiments, all of the sensor data 112 for the person istransmitted wirelessly via the X10 protocol to a data monitor PC whichis located in the living unit of the person. The data monitor may add adate-time stamp for each sensor event and may log it as the sensor datainto a file that is periodically sent to a dedicated central serverwhich stores the data in a relational database. The data monitors may beconnected to the central server through a dedicated local network, forsecurity purposes. In addition, as a precaution, identifiers may bestripped from the data before transmission.

FIG. 25 is a diagram 2500 of predicted pulse pressure from the sensordata 112 and measured pulse pressure, according to an example embodiment

FIG. 26 is a diagram 2600 of a comparison of Euclidean distance for aperson, according to an example embodiment. The diagram 2600 may begenerated as a result of the operations performed at block 1108 (seeFIG. 11).

FIG. 27 is a diagram 2700 of multiple density maps associated with forthe diagram 2600.

FIG. 28 shows a block diagram of a machine in the example form of acomputer system 2800 within which a set of instructions may be executedcausing the machine to perform any one or more of the methods,processes, operations, or methodologies discussed herein. The operatordevice 102, the provider device 106, or both may include thefunctionality of the one or more computer systems 2800.

In an example embodiment, the machine operates as a standalone device ormay be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a server computer, a client computer, a personal computer(PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant(PDA), a cellular telephone, a web appliance, a network router, switchor bridge, a kiosk, a point of sale (POS) device, a cash register, anAutomated Teller Machine (ATM), or any machine capable of executing aset of instructions (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 2800 includes a processor 2812 (e.g., acentral processing unit (CPU) a graphics processing unit (GPU) or both),a main memory 2804 and a static memory 2806, which communicate with eachother via a bus 2808. The computer system 2800 may further include avideo display unit 2810 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 2800 also includes analphanumeric input device 2812 (e.g., a keyboard), a cursor controldevice 2814 (e.g., a mouse), a drive unit 2816, a signal generationdevice 2818 (e.g., a speaker) and a network interface device 2820.

The drive unit 2816 includes a machine-readable medium 2822 on which isstored one or more sets of instructions (e.g., software 2824) embodyingany one or more of the methodologies or functions described herein. Thesoftware 2824 may also reside, completely or at least partially, withinthe main memory 2804 and/or within the processor 2812 during executionthereof by the computer system 2800, the main memory 2804 and theprocessor 2812 also constituting machine-readable media.

The software 2824 may further be transmitted or received over a network2826 via the network interface device 2820.

While the machine-readable medium 2822 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention. The term “machine-readablemedium” shall accordingly be taken to include, but not be limited to,solid-state memories, and optical media, and magnetic media. In someembodiments, the machine-readable medium is a non-transitory machinereadable medium.

Certain systems, apparatus, applications or processes are describedherein as including a number of modules. A module may be a unit ofdistinct functionality that may be presented in software, hardware, orcombinations thereof. When the functionality of a module is performed inany part through software, the module includes a machine-readablemedium. The modules may be regarded as being communicatively coupled.

In an example embodiment, sensor data may be accessed from a pluralityof motion sensors and a bed sensor deployed in a living unit for a firsttime period. An activity pattern for the first time period may beidentified based on at least a portion of sensor data associated withthe first time period. The activity pattern may represent a physical andcognitive health condition of a person residing in the living unit.Additional sensor data may be accessed from the plurality of motionsensors and the bed sensor deployed in the living unit for a second timeperiod. The second time period may occur after the first time period. Adetermination of whether a deviation of the activity pattern of thefirst time period has occurred for the second time period may beperformed. An alert may be generated based on a determination that thederivation has occurred.

In an example embodiment, health data of a person may be accessed for afirst time period. Sensor data from a plurality of motion sensors and abed sensor deployed in a living unit may be accessed for the first timeperiod. The person may live in the living unit. Health data may becorrelated to at least a portion of the sensor data for the first timeperiod. Additional sensor data may be accessed from the plurality ofmotion sensors and the bed sensor deployed in the living unit for asecond time period. The second time period may occurring after the firsttime period. A determination of whether a change in a health conditionof the person has occurred may be made based on the additional sensordata and correlation of the health data to at least the portion of thesensor data for the first time period.

In an example embodiment, sensor data may be accessed from a pluralityof motion sensors and a bed sensor deployed in a living unit for a timeperiod. A display may be generated based on access of the sensor dataassociated with the time period.

In an example embodiment, a first density map and a second density mapmay be accessed. The first density map may have a plurality of firstcolor mappings. The second density map may have a plurality of secondcolor mappings. A particular first color mapping may have a color basedon density and being associated with a position based on a particularhour and a particular day. Density may be based on a number of motionsensor hits during the particular hour and a determination of theaway-from-home time period. A dis-similarity between the first densitymap and the second density map may be computed based on a textualfeature of the first density map and the second density map. Acomputational result may be generated based on computing thedis-similarity.

In an example embodiment, a plurality of feature clusters may begenerated for a time period. The time period may include a plurality ofdays. A particular feature cluster may be associated with a plurality offeature vectors. A particular feature vector may be associated withsensor data from at least some of a plurality of motion sensors and abed sensor deployed in a living unit. Additional sensor data associateda particular feature for a different time period may be accessed. Adetermination of whether the additional sensor data falls within theplurality of feature clusters or belongs in a new cluster may be made. Anotification may be generated based on a result of a determination.

Thus, methods and systems for an integrated network have been described.Although embodiments of the present invention have been described withreference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader spirit and scope of the embodimentsof the invention. Accordingly, the specification and drawings are to beregarded in an illustrative rather than a restrictive sense.

Various activities described with respect to the methods identifiedherein can be executed in serial or parallel fashion. Although “End”blocks are shown in the flowcharts, the methods may be performedcontinuously.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter may lie in less than all features of asingle disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment.

1. A method comprising: accessing sensor data from a plurality of motionsensors and a bed sensor deployed in a living unit for a first timeperiod; identifying an activity pattern for the first time period basedon at least a portion of sensor data associated with the first timeperiod, the activity pattern representing a physical and cognitivehealth condition of a person residing in the living unit; accessingadditional sensor data from the plurality of motion sensors and the bedsensor deployed in the living unit for a second time period, the secondtime period occurring after the first time period; determining whether adeviation of the activity pattern of the first time period has occurredfor the second time period; and generating an alert based on adetermination that the derivation has occurred.
 2. The method of claim1, wherein the activity pattern includes a plurality of features and thedeviation is associated with a particular feature of the plurality offeatures.
 3. The method of claim 1, further comprising: identifying theactivity pattern for the second time period based on access of theadditional sensor data associated with the second time period, whereindetermining whether the deviation has occurred includes determiningwhether the deviation of the activity pattern of the second time periodfrom the activity pattern of the first time period exceeds a threshold4. The method of claim 1, further comprising: analyzing health dataassociated with the person, wherein generation of the alert is based onwhen the activity pattern of the second time period from the activitypattern of the first time period exceeds the threshold and analysis ofthe health data.
 6. The method of claim 1, further comprising: accessingthe sensor data from a stove sensor deployed in the living unit; andaccessing the additional sensor data from the stove sensor deployed inthe living unit.
 7. The method of claim 1, wherein the plurality ofmotion sensors and the bed sensor are passive, non-wearable sensors. 8.The method of claim 1, further comprising: identifying at least aportion of the sensor data associated with the first time period asbeing associated with the person, wherein identification of the activitypattern for the first time period is based on at least the portion ofsensor data associated with the first time period associated with theperson.
 9. The method of claim 1, wherein the first period of time has asame time duration as the second period of time.
 10. The method of claim1, further comprising: calculating statistical parameters of at least aportion of the sensor data for the first time period, whereindetermining whether the deviation has occurred includes determiningwhether at least a portion of the additional sensor data for the secondtime period is outside of a threshold, wherein the threshold is based onthe statistical parameters.
 11. The method of claim 1, furthercomprising: transmitting the alert including a link to a web interface,the web interface including the sensor data of the second time period inthe context of the sensor data of the first time period.
 12. The methodof claim 11, further comprising: receiving a feedback response to thealert, feedback response including feedback regarding clinical relevanceof the alert; taking action based on receipt of the feedback response.13. The method of claim 12, wherein taking the action comprises:adjusting the threshold, the sensor parameter used for the alert, orboth based on the receipt of the feedback response.
 14. The method ofclaim 1, wherein the activity pattern includes a plurality of features.15. A method comprising: accessing health data of a person for a firsttime period; accessing sensor data from a plurality of motion sensorsand a bed sensor deployed in a living unit for the first time period,the person residing in the living unit; correlating the health data toat least a portion of the sensor data for the first time period;accessing additional sensor data from the plurality of motion sensorsand the bed sensor deployed in the living unit for a second time period,the second time period occurring after the first time period; anddetermining whether a change in a health condition of the person hasoccurred based on the additional sensor data and correlation of thehealth data to at least the portion of the sensor data for the firsttime period.
 16. The method of claim 15, further comprising: generatingan alert when a determination is made that the change in the healthcondition has occurred.
 17. The method of claim 15, wherein the healthcondition is pulse pressure, pulse pressure being the difference betweensystolic blood pressure (SBP) and the diastolic blood pressure (DBP).18. A method comprising: accessing sensor data from a plurality ofmotion sensors and a bed sensor deployed in a living unit for a timeperiod; and generating a display based on access of the sensor dataassociated with the time period.
 19. The method of claim 18, wherein thesensor data is grouped on the display based on a plurality ofcategories, the plurality of categories include motion, pulse,breathing, and restlessness.
 20. The method of claim 18, furthercomprising: receiving a selection of a person and a date range, whereinaccessing sensor data from the plurality of motion sensors and the bedsensor for the time period is based on receipt of the selection.
 21. Themethod of claim 18, further comprising: receiving a time intervalmodification request, wherein generation of the display is based onaccess of the sensor data associated with the time period and receipt ofthe time interval modification request.
 22. The method of claim 18,further comprising: receiving a time increment modification request,wherein generation of the display is based on access of the sensor dataassociated with the time period and receipt of the time incrementmodification request.
 23. The method of claim 18, further comprising:determining an away-from-home time period for a person associated withthe living unit during the time period, wherein generation of thedisplay is based on access of the sensor data and a determination of theaway-from-home time period.
 24. The method of claim 23, whereindetermining the away-from home time period comprises: analyzing thesensor data to determine whether a living unit departure sensor sequenceand a living unit return sensor sequence has occurred; and calculating atime difference between occurrence of the living unit departure sensorsequence and occurrence of the living unit return sensor sequence,wherein the away-from home time period is based on the time difference.25. The method of claim 24, wherein analyzing the sensor data comprises:applying fuzzy logic to at least a portion of the sensor data todetermine whether a living unit departure sensor sequence and a livingunit return sensor sequence has occurred.
 26. The method of claim 23,further comprising: computing a number of motion sensor hits for aplurality of hours, a single motion sensor hit being associated with aparticular motion sensor of the plurality of motion sensors, the timeperiod including the plurality of hours; and calculating density for theplurality of hours, the density for a particular hour of the pluralityof hours being based on the number of motion sensor hits during theparticular hour and the determination of the away-from-home time period,wherein generation of the display is based on calculation of thedensity.
 27. The method of claim 26, wherein generation of the displaycomprises: selecting a plurality of color mappings, a particular colormapping having a color based on the density and being associated with aposition based on the particular hour and a particular day, the timeperiod including a plurality of days, wherein generation of the displayis based on selection of the plurality of color mappings.
 28. A methodcomprising: accessing a first density map and a second density map, thefirst density map having a plurality of first color mappings, the seconddensity map having a plurality of second color mappings, a particularfirst color mapping having a color based on density and being associatedwith a position based on a particular hour and a particular day, thedensity being based on a number of motion sensor hits during theparticular hour and a determination of the away-from-home time period;computing a dis-similarity between the first density map and the seconddensity map based on a textual feature of the first density map and thesecond density map; and generating a computational result based oncomputing the dis-similarity.
 29. The method of claim 28, wherein thetextual features include spatial, frequency, and perceptual properties.30. A method comprising: generating a plurality of feature clusters fora time period, the time period including a plurality of days, aparticular feature cluster associated with a plurality of featurevectors, a particular feature vector associated sensor data from atleast some of a plurality of motion sensors and a bed sensor deployed ina living unit; accessing an additional sensor data associated aparticular feature for a different time period; determining whether theadditional sensor data falls within the plurality of feature clusters orbelongs in a new cluster; and generating a notification based on aresult of a determination.
 31. The method of claim 30, whereingenerating the notification comprises: generating a cluster additionnotification based on a determination that the additional sensor datafalls within the plurality of feature clusters.
 32. The method of claim30, wherein generating the notification comprises: generating a newcluster notification based on a determination that the additional sensordata belongs in the new cluster.
 33. A non-transitory machine-readablemedium comprising instructions, which when executed by one or moreprocessors, cause the one or more processors to perform the followingoperations: access sensor data from a plurality of motion sensors and abed sensor deployed in a living unit for a first time period; identifyan activity pattern for the first time period based on at least aportion of sensor data associated with the first time period, theactivity pattern representing a physical and cognitive health conditionof a person residing in the living unit; access additional sensor datafrom the plurality of motion sensors and the bed sensor deployed in theliving unit for a second time period, the second time period occurringafter the first time period; determine whether a deviation of theactivity pattern of the first time period has occurred for the secondtime period; and generate an alert based on a determination that thederivation has occurred.
 34. A system comprising: a processor and amemory coupled to the processor; a sensor data module deployed in thememory and executed by the processor to access sensor data from aplurality of motion sensors and a bed sensor deployed in a living unitfor a first time period and to access additional sensor data from theplurality of motion sensors and the bed sensor deployed in the livingunit for a second time period, the second time period occurring afterthe first time period; an activity pattern identification moduledeployed in the memory and executed by the processor to identify anactivity pattern for the first time period based on at least a portionof sensor data associated with the first time period, the activitypattern representing a physical and cognitive health condition of aperson residing in the living unit; a deviation module deployed in thememory and executed by the processor to determine whether a deviation ofthe activity pattern of the first time period identified by the activityidentification module has occurred for the second time period; and adisplay generation module deployed in the memory and executed by theprocessor to generate an alert based on a determination by the deviationmodule that the derivation has occurred.